AUV driven monitoring can generate large quantities of imagery. For example, an AUV deployed in Western Australia collected more than 15,000 stereo image pairs each day and was deployed between 10 and 12 days each year [1]}. Manual analysis of such a large number of images per deployment (150,000 to 200,000 stereo image pairs) takes a significant amount of time and effort and is the major bottleneck in data acquisition from AUV surveys. In order to promptly identify changes in benthic species, especially dominant habitat formers (such as kelps and corals), it is necessary to match image-analysis time to surveying time so data can be analyzed rapidly and identification of change patterns can be accomplished. Automatic classification is critical to speed up image analysis and consequently automatic classification of benthic species has raised interest in ecologists and computer scientists (such as [2]}, [3]}, [4]}, [5]}, [6]}). Nonetheless, automated classification of AUV collected imagery is challenging because images are captured in dynamic shallow water with little to no control on lighting and significant variations in what is visible and how it is perceived.
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